Coprime array offers a larger array aperture than uniform linear array with the same number of physical sensors, and has a better spatial resolution with increased degrees of freedom. However, when it comes to the problem of adaptive beamforming, the existing adaptive beamforming algorithms designed for the general array cannot take full advantage of coprime feature offered by the coprime array. In this paper, we propose a novel coprime array adaptive beamforming algorithm, where both robustness and efficiency are well balanced. Specifically, we first decompose the coprime array into a pair of sparse uniform linear subarrays and process their received signals separately. According to the property of coprime integers, the direction-of-arrival (DOA) can be uniquely estimated for each source by matching the super-resolution spatial spectra of the pair of sparse uniform linear subarrays. Further, a joint covariance matrix optimization problem is formulated to estimate the power of each source. The estimated DOAs and their corresponding power are utilized to reconstruct the interference-plus-noise covariance matrix and estimate the signal steering vector. Theoretical analyses are presented in terms of robustness and efficiency, and simulation results demonstrate the effectiveness of the proposed coprime array adaptive beamforming algorithm.

Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years. In contrast to traditional wireless communication methods that focus on the transmission of abstract symbols, semantic communication approaches attempt to achieve better transmission efficiency by only sending the semantic-related information of the source data. In this paper, we consider semantic-oriented speech transmission which transmits only the semantic-relevant information over the channel for the speech recognition task, and a compact additional set of semantic-irrelevant information for the speech reconstruction task. We propose a novel end-to-end DL-based transceiver which extracts and encodes the semantic information from the input speech spectrums at the transmitter and outputs the corresponding transcriptions from the decoded semantic information at the receiver. In particular, we employ a soft alignment module and a redundancy removal module to extract only the text-related semantic features while dropping semantically redundant content, greatly reducing the amount of semantic redundancy compared to existing methods. We also propose a semantic correction module to further correct the predicted transcription with semantic knowledge by leveraging a pretrained language model. For the speech to speech transmission, we further include a CTC alignment module that extracts a small number of additional semantic-irrelevant but speech-related information, such as duration, pitch, power and speaker identification of the speech for the better reconstruction of the original speech signals at the receiver. We also introduce a two-stage training scheme which speeds up the training of the proposed DL model. The simulation results confirm that our proposed method outperforms current methods in terms of the accuracy of the predicted text for the speech to text transmission and the quality of the recovered speech signals for the speech to speech transmission, and significantly improves transmission efficiency. More specifically, the proposed method only sends 16% of the amount of the transmitted symbols required by the existing methods while achieving about a 10% reduction in WER for the speech to text transmission. For the speech to speech transmission, it results in an even more remarkable improvement in terms of transmission efficiency with only 0.2% of the amount of the transmitted symbols required by the existing method while preserving the comparable quality of the reconstructed speech signals.

Recently, narrowband Internet of Things (NB-IoT), one of the most promising low power wide area (LPWA) technologies, has attracted much attention from both academia and industry. It has great potential to meet the huge demand for machine-type communications in the era of IoT. To facilitate research on and application of NB-IoT, in this paper, we design a system that includes NB devices, an IoT cloud platform, an application server, and a user app. The core component of the system is to build a development board that integrates an NB-IoT communication module and a subscriber identification module, a micro-controller unit and power management modules. We also provide a firmware design for NB device wake-up, data sensing, computing and communication, and the IoT cloud configuration for data storage and analysis. We further introduce a framework on how to apply the proposed system to specific applications. The proposed system provides an easy approach to academic research as well as commercial applications.

In this letter, we propose a coprime array interpolation approach to provide an off-grid direction-of-arrival (DOA) estimation. Through array interpolation, a uniform linear array (ULA) with the same aperture is generated from the deterministic non-uniform coprime array. Taking the observed correlations calculated from the signals received at the coprime array, a gridless convex optimization problem is formulated to recover all the rows and columns of the unknown correlation matrix entries corresponding to the interpolated sensors. The optimized Hermitian positive semidefinite Toeplitz matrix functions as the covariance matrix of the interpolated ULA, which enables to resolve off-grid sources. Simulation results demonstrate that the proposed array interpolation-based DOA estimation algorithm achieves improved performance as compared to existing coarray-based DOA estimation algorithms in terms of the number of achievable degrees-of-freedom and estimation accuracy.

Collaborative sensing leverages the cooperation of a collection of sensors to complete a large-scale sensing task in Internet of Things (IoT). Although some previous studies have reviewed the literature of collaborative sensing in sensor networks, there still lacks a systematic and holistic overview with the consideration of practical application needs in IoT. In this article, we highlight that sensing application is coupled with sensing mechanism and sensing collaboration, and comprehensively survey the sensing mechanism, collaboration and applications in the context of IoT. Specifically, we first give an introduction of sensing technologies widely employed in IoT applications. Then, we systematically expatiate on more design issues for collaborative sensing, i.e., sensing models, deployment methods, sensing scheduling strategies and metrics for sensing quality. Next, the network-wide sensing collaboration is expatiated through three fundamental types of coverage problems as well as their solutions and insightful observations. Furthermore, we discuss promising sensing applications and elaborate how the sensing problem is established from the realistic application requirements and how the sensing quality impacts the application performance. Finally, we discuss several inspiring future research directions.